"""
Copyright 2013 Steven Diamond

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""

import collections
import itertools
import math

import numpy as np
import numpy.linalg as LA
import pytest

# Tests atoms by calling them with a constant value.
import cvxpy as cp
import cvxpy.interface as intf
from cvxpy.error import SolverError
from cvxpy.expressions.constants import Constant, Parameter
from cvxpy.expressions.variable import Variable
from cvxpy.problems.problem import Problem
from cvxpy.reductions.solvers.defines import INSTALLED_SOLVERS
from cvxpy.settings import CVXOPT, ECOS, MOSEK, OSQP, ROBUST_KKTSOLVER, SCS

ROBUST_CVXOPT = "robust_cvxopt"
SOLVER_TO_TOL = {SCS: 1e-2,
                 ECOS: 1e-7,
                 OSQP: 1e-1}
SOLVERS_TO_TRY = [ECOS, SCS, OSQP]
# Test CVXOPT if installed.
if CVXOPT in INSTALLED_SOLVERS:
    SOLVERS_TO_TRY += [CVXOPT, ROBUST_CVXOPT]
    SOLVER_TO_TOL[CVXOPT] = 1e-7
    SOLVER_TO_TOL[ROBUST_CVXOPT] = 1e-7

# Test MOSEK if installed.
if MOSEK in INSTALLED_SOLVERS:
    SOLVERS_TO_TRY.append(MOSEK)
    SOLVER_TO_TOL[MOSEK] = 1e-6

v_np = np.array([-1., 2, -2]).T

# Defined here to be used in KNOWN_SOLVER_ERRORS


def log_sum_exp_axis_0(x): return cp.log_sum_exp(x, axis=0, keepdims=True)  # noqa E371
def log_sum_exp_axis_1(x): return cp.log_sum_exp(x, axis=1)  # noqa E371


# map from solver name to a list of strings for atoms that fail.
#   old example: KNOWN_SOLVER_ERRORS[cp.MOSEK] = ['xexp']
KNOWN_SOLVER_ERRORS = collections.defaultdict(list)

atoms_minimize = [
    (cp.abs, (2, 2), [[[-5, 2], [-3, 1]]],
     Constant([[5, 2], [3, 1]])),
    (lambda x: cp.cumsum(x, axis=1), (2, 2), [[[-5, 2], [-3, 1]]],
     Constant([[-5, 2], [-8, 3]])),
    (lambda x: cp.cumsum(x, axis=0), (2, 2), [[[-5, 2], [-3, 1]]],
     Constant([[-5, -3], [-3, -2]])),
    (lambda x: cp.cummax(x, axis=1), (2, 2), [[[-5, 2], [-3, 1]]],
     Constant([[-5, 2], [-3, 2]])),
    (lambda x: cp.cummax(x, axis=0), (2, 2), [[[-5, 2], [-3, 1]]],
     Constant([[-5, 2], [-3, 1]])),
    (cp.diag, (2,), [[[-5, 2], [-3, 1]]], Constant([-5, 1])),
    (cp.diag, (2, 2), [[-5, 1]], Constant([[-5, 0], [0, 1]])),
    (cp.exp, (2, 2), [[[1, 0], [2, -1]]],
     Constant([[math.e, 1], [math.e**2, 1.0 / math.e]])),
    (lambda x: cp.xexp(cp.pos(x)), (2, 2), [[[1, 3], [2, .5]]],
     Constant([[math.e, 3 * math.e**3], [2 * math.e**2, 0.5 * math.e**.5]])),
    (cp.huber, (2, 2), [[[0.5, -1.5], [4, 0]]],
     Constant([[0.25, 2], [7, 0]])),
    (lambda x: cp.huber(x, 2.5), (2, 2), [[[0.5, -1.5], [4, 0]]],
     Constant([[0.25, 2.25], [13.75, 0]])),
    (cp.inv_pos, (2, 2), [[[1, 2], [3, 4]]],
     Constant([[1, 1.0 / 2], [1.0 / 3, 1.0 / 4]])),
    (lambda x: (x + Constant(0))**-1, (2, 2), [[[1, 2], [3, 4]]],
     Constant([[1, 1.0 / 2], [1.0 / 3, 1.0 / 4]])),
    (cp.kl_div, tuple(), [math.e, 1], Constant([1])),
    (cp.kl_div, tuple(), [math.e, math.e], Constant([0])),
    (cp.kl_div, (2,), [[math.e, 1], 1], Constant([1, 0])),
    (cp.rel_entr, tuple(), [math.e, 1], Constant([math.e])),
    (cp.rel_entr, tuple(), [math.e, math.e], Constant([0])),
    (cp.rel_entr, (2,), [[math.e, 1], 1], Constant([math.e, 0])),
    # kron with variable in the right operand
    (lambda x: cp.kron(np.array([[1, 2], [3, 4]]), x), (4, 4),
     [np.array([[5, 6], [7, 8]])],
     Constant(np.kron(np.array([[1, 2], [3, 4]]), np.array([[5, 6], [7, 8]])))),
    (lambda x: cp.kron(np.array([[1, 2], [3, 4], [5, 6]]), x), (6, 4),
     [np.array([[5, 6], [7, 8]])],
     Constant(np.kron(np.array([[1, 2], [3, 4], [5, 6]]), np.array([[5, 6], [7, 8]])))),
    (lambda x: cp.kron(np.array([[1, 2], [3, 4]]), x), (6, 4),
     [np.array([[5, 6], [7, 8], [9, 10]])],
     Constant(np.kron(np.array([[1, 2], [3, 4]]), np.array([[5, 6], [7, 8], [9, 10]])))),

    # kron with variable in the left operand
    (lambda x: cp.kron(x, np.array([[1, 2], [3, 4]])), (4, 4),
     [np.array([[5, 6], [7, 8]])],
     Constant(np.kron(np.array([[5, 6], [7, 8]]), np.array([[1, 2], [3, 4]])))),
    (lambda x: cp.kron(x, np.array([[1, 2], [3, 4], [5, 6]])), (6, 4),
     [np.array([[5, 6], [7, 8]])],
     Constant(np.kron(np.array([[5, 6], [7, 8]]), np.array([[1, 2], [3, 4], [5, 6]])))),
    (lambda x: cp.kron(x, np.array([[1, 2], [3, 4]])), (6, 4),
     [np.array([[5, 6], [7, 8], [9, 10]])],
     Constant(np.kron(np.array([[5, 6], [7, 8], [9, 10]]), np.array([[1, 2], [3, 4]])))),
    (cp.lambda_max, tuple(), [[[2, 0], [0, 1]]], Constant([2])),
    (cp.lambda_max, tuple(), [[[2, 0, 0], [0, 3, 0], [0, 0, 1]]], Constant([3])),
    (cp.lambda_max, tuple(), [[[5, 7], [7, -3]]], Constant([9.06225775])),
    (lambda x: cp.lambda_sum_largest(x, 2), tuple(),
     [[[1, 2, 3], [2, 4, 5], [3, 5, 6]]], Constant([11.51572947])),
    (cp.log_sum_exp, tuple(), [[[5, 7], [0, -3]]], Constant([7.1277708268])),
    (log_sum_exp_axis_0, (1, 2),
     [[[5, 7, 1], [0, -3, 6]]], Constant([[7.12910890], [6.00259878]])),
    (log_sum_exp_axis_1, (3,),
     [[[5, 7, 1], [0, -3, 6]]], Constant([5.00671535, 7.0000454, 6.0067153])),
    (cp.logistic, (2, 2),
     [
        [[math.log(5), math.log(7)],
         [0, math.log(0.3)]]],
     Constant(
        [[math.log(6), math.log(8)],
         [math.log(2), math.log(1.3)]])),
    (cp.matrix_frac, tuple(), [[1, 2, 3],
                               [[1, 0, 0],
                                [0, 1, 0],
                                [0, 0, 1]]], Constant([14])),
    (cp.matrix_frac, tuple(), [[1, 2, 3],
                               [[67, 78, 90],
                                [78, 94, 108],
                                [90, 108, 127]]], Constant([0.46557377049180271])),
    (cp.matrix_frac, tuple(), [[[1, 2, 3],
                                [4, 5, 6]],
                               [[67, 78, 90],
                                [78, 94, 108],
                                [90, 108, 127]]], Constant([0.768852459016])),
    (cp.maximum, (2,), [[-5, 2], [-3, 1], 0, [-1, 2]], Constant([0, 2])),
    (cp.maximum, (2, 2), [[[-5, 2], [-3, 1]], 0, [[5, 4], [-1, 2]]],
     Constant([[5, 4], [0, 2]])),
    (cp.max, tuple(), [[[-5, 2], [-3, 1]]], Constant([2])),
    (cp.max, tuple(), [[-5, -10]], Constant([-5])),
    (lambda x: cp.max(x, axis=0, keepdims=True), (1, 2),
     [[[-5, 2], [-3, 1]]], Constant([[2], [1]])),
    (lambda x: cp.max(x, axis=1), (2,), [[[-5, 2], [-3, 1]]], Constant([-3, 2])),
    (lambda x: cp.norm(x, 2), tuple(), [v_np], Constant([3])),
    (lambda x: cp.norm(x, "fro"), tuple(), [[[-1, 2], [3, -4]]],
     Constant([5.47722557])),
    (lambda x: cp.norm(x, 1), tuple(), [v_np], Constant([5])),
    (lambda x: cp.norm(x, 1), tuple(), [[[-1, 2], [3, -4]]],
     Constant([7])),
    (lambda x: cp.norm(x, "inf"), tuple(), [v_np], Constant([2])),
    (lambda x: cp.norm(x, "inf"), tuple(), [[[-1, 2], [3, -4]]],
     Constant([6])),
    (lambda x: cp.norm(x, "nuc"), tuple(), [[[2, 0], [0, 1]]], Constant([3])),
    (lambda x: cp.norm(x, "nuc"), tuple(), [[[3, 4, 5], [6, 7, 8], [9, 10, 11]]],
     Constant([23.173260452512931])),
    (lambda x: cp.norm(x, "nuc"), tuple(), [[[3, 4, 5], [6, 7, 8]]],
     Constant([14.618376738088918])),
    (lambda x: cp.sum_largest(cp.abs(x), 3), tuple(), [[1, 2, 3, -4, -5]], Constant([5 + 4 + 3])),
    (lambda x: cp.dotsort(cp.abs(x), np.array([2, 1, 0.1])), tuple(), [[1, 2, 3, -4, -5]],
     Constant([10 + 4 + .3])),
    (lambda x: cp.mixed_norm(x, 1, 1), tuple(), [[[1, 2], [3, 4], [5, 6]]],
     Constant([21])),
    (lambda x: cp.mixed_norm(x, 1, 1), tuple(), [[[1, 2, 3], [4, 5, 6]]],
     Constant([21])),
    # (lambda x: mixed_norm(x, 2, 1), tuple(), [[[3, 1], [4, math.sqrt(3)]]],
    #     Constant([7])),
    (lambda x: cp.mixed_norm(x, 1, 'inf'), tuple(), [[[1, 4], [5, 6]]],
     Constant([10])),

    (cp.pnorm, tuple(), [[1, 2, 3]], Constant([3.7416573867739413])),
    (lambda x: cp.pnorm(x, 1), tuple(), [[1.1, 2, -3]], Constant([6.1])),
    (lambda x: cp.pnorm(x, 2), tuple(), [[1.1, 2, -3]], Constant([3.7696153649941531])),
    (lambda x: cp.pnorm(x, 2, axis=0), (2,),
     [[[1, 2], [3, 4]]], Constant([math.sqrt(5), 5.]).T),
    (lambda x: cp.pnorm(x, 2, axis=1), (2,),
     [[[1, 2], [4, 5]]], Constant([math.sqrt(17), math.sqrt(29)])),
    (lambda x: cp.pnorm(x, 'inf'), tuple(), [[1.1, 2, -3]], Constant([3])),
    (lambda x: cp.pnorm(x, 3), tuple(), [[1.1, 2, -3]], Constant([3.3120161866074733])),
    (lambda x: cp.pnorm(x, 5.6), tuple(), [[1.1, 2, -3]], Constant([3.0548953718931089])),
    (lambda x: cp.pnorm(x, 1.2), tuple(),
     [[[1, 2, 3], [4, 5, 6]]], Constant([15.971021676279573])),

    (cp.pos, tuple(), [8], Constant([8])),
    (cp.pos, (2,), [[-3, 2]], Constant([0, 2])),
    (cp.neg, (2,), [[-3, 3]], Constant([3, 0])),


    (lambda x: cp.power(x, 1), tuple(), [7.45], Constant([7.45])),
    (lambda x: cp.power(x, 2), tuple(), [7.45], Constant([55.502500000000005])),
    (lambda x: cp.power(x, -1), tuple(), [7.45], Constant([0.1342281879194631])),
    (lambda x: cp.power(x, -.7), tuple(), [7.45], Constant([0.24518314363015764])),
    (lambda x: cp.power(x, -1.34), tuple(), [7.45], Constant([0.06781263100321579])),
    (lambda x: cp.power(x, 1.34), tuple(), [7.45], Constant([14.746515290825071])),

    (cp.quad_over_lin, tuple(), [[[-1, 2, -2], [-1, 2, -2]], 2], Constant([2 * 4.5])),
    (cp.quad_over_lin, tuple(), [v_np, 2], Constant([4.5])),
    (lambda x: cp.norm(x, 2), tuple(), [[[2, 0], [0, 1]]], Constant([2])),
    (lambda x: cp.norm(x, 2), tuple(),
     [[[3, 4, 5], [6, 7, 8], [9, 10, 11]]], Constant([22.368559552680377])),
    (lambda x: cp.scalene(x, 2, 3), (2, 2), [[[-5, 2], [-3, 1]]], Constant([[15, 4], [9, 2]])),
    (cp.square, (2, 2), [[[-5, 2], [-3, 1]]], Constant([[25, 4], [9, 1]])),
    (cp.sum, tuple(), [[[-5, 2], [-3, 1]]], Constant([-5])),
    (lambda x: cp.sum(x, axis=0), (2,), [[[-5, 2], [-3, 1]]], Constant([-3, -2])),
    (lambda x: cp.sum(x, axis=1), (2,), [[[-5, 2], [-3, 1]]], Constant([-8, 3])),
    (lambda x: (x + Constant(0))**2, (2, 2), [[[-5, 2], [-3, 1]]], Constant([[25, 4], [9, 1]])),
    (lambda x: cp.sum_largest(x, 3), tuple(), [[1, 2, 3, 4, 5]], Constant([5 + 4 + 3])),
    (lambda x: cp.sum_largest(x, 3), tuple(),
     [[[3, 4, 5], [6, 7, 8], [9, 10, 11]]], Constant([9 + 10 + 11])),
    (cp.sum_squares, tuple(), [[[-1, 2], [3, -4]]], Constant([30])),
    (cp.trace, tuple(), [[[3, 4, 5], [6, 7, 8], [9, 10, 11]]], Constant([3 + 7 + 11])),
    (cp.trace, tuple(), [[[-5, 2], [-3, 1]]], Constant([-5 + 1])),
    (cp.tv, tuple(), [[1, -1, 2]], Constant([5])),
    (cp.tv, tuple(), [[1, -1, 2]], Constant([5])),
    (cp.tv, tuple(), [[[-5, 2], [-3, 1]]], Constant([math.sqrt(53)])),
    (cp.tv, tuple(), [[[-5, 2], [-3, 1]], [[6, 5], [-4, 3]], [[8, 0], [15, 9]]],
     Constant([LA.norm([7, -1, -8, 2, -10, 7])])),
    (cp.tv, tuple(), [[[3, 4, 5], [6, 7, 8], [9, 10, 11]]], Constant([4 * math.sqrt(10)])),
    (cp.upper_tri, (3,), [[[3, 4, 5], [6, 7, 8], [9, 10, 11]]], Constant([6, 9, 10])),
    # # Advanced indexing.
    (lambda x: x[[1, 2], [0, 2]], (2,),
     [[[3, 4, 5], [6, 7, 8], [9, 10, 11]]], Constant([4, 11])),
    (lambda x: x[[1, 2]], (2, 2), [[[3, 4, 5], [6, 7, 8]]], Constant([[4, 5], [7, 8]])),
    (lambda x: x[np.array([[3, 4, 5], [6, 7, 8]]).T % 2 == 0], (2,), [[[3, 4, 5], [6, 7, 8]]],
     Constant([6, 4, 8])),
    (lambda x: x[2:0:-1], (2,), [[3, 4, 5]], Constant([5, 4])),
    (lambda x: x[2::-1], (3,), [[3, 4, 5]], Constant([5, 4, 3])),
    (lambda x: x[3:0:-1], (2,), [[3, 4, 5]], Constant([5, 4])),
    (lambda x: x[3::-1], (3,), [[3, 4, 5]], Constant([5, 4, 3])),

]


atoms_maximize = [
    (cp.entr, (2, 2), [[[1, math.e], [math.e**2, 1.0 / math.e]]],
     Constant([[0, -math.e], [-2 * math.e**2, 1.0 / math.e]])),
    (cp.log_det, tuple(),
     [[[20, 8, 5, 2],
       [8, 16, 2, 4],
       [5, 2, 5, 2],
       [2, 4, 2, 4]]], Constant([7.7424020218157814])),
    (cp.geo_mean, tuple(), [[4, 1]], Constant([2])),
    (cp.geo_mean, tuple(), [[0.01, 7]], Constant([0.2645751311064591])),
    (cp.geo_mean, tuple(), [[63, 7]], Constant([21])),
    (cp.geo_mean, tuple(), [[1, 10]], Constant([math.sqrt(10)])),
    (lambda x: cp.geo_mean(x, [1, 1]), tuple(), [[1, 10]], Constant([math.sqrt(10)])),
    (lambda x: cp.geo_mean(x, [.4, .8, 4.9]), tuple(),
     [[.5, 1.8, 17]], Constant([10.04921378316062])),
    (cp.harmonic_mean, tuple(), [[1, 2, 3]], Constant([1.6363636363636365])),
    (cp.harmonic_mean, tuple(), [[2.5, 2.5, 2.5, 2.5]], Constant([2.5])),
    (cp.harmonic_mean, tuple(), [[1e-8, 1, 2]], Constant([0])),

    (lambda x: cp.diff(x, 0), (3,), [[1, 2, 3]], Constant([1, 2, 3])),
    (cp.diff, (2,), [[1, 2, 3]], Constant([1, 1])),
    (cp.diff, tuple(), [[1.1, 2.3]], Constant([1.2])),
    (lambda x: cp.diff(x, 2), tuple(), [[1, 2, 3]], Constant([0])),
    (cp.diff, (3,), [[2.1, 1, 4.5, -.1]], Constant([-1.1, 3.5, -4.6])),
    (lambda x: cp.diff(x, 2), (2,), [[2.1, 1, 4.5, -.1]], Constant([4.6, -8.1])),
    (lambda x: cp.diff(x, 1, axis=0), (1, 2), [np.array([[-5, -3], [2, 1]])],
     Constant([[7], [4]])),
    (lambda x: cp.diff(x, 1, axis=1), (2, 1), [np.array([[-5, -3], [2, 1]])],
     Constant([[2, -1]])),

    (lambda x: cp.pnorm(x, .5), tuple(), [[1.1, 2, .1]], Constant([7.724231543909264])),
    (lambda x: cp.pnorm(x, -.4), tuple(), [[1.1, 2, .1]], Constant([0.02713620334])),
    (lambda x: cp.pnorm(x, -1), tuple(), [[1.1, 2, .1]], Constant([0.0876494023904])),
    (lambda x: cp.pnorm(x, -2.3), tuple(), [[1.1, 2, .1]], Constant([0.099781528576])),

    (cp.lambda_min, tuple(), [[[2, 0], [0, 1]]], Constant([1])),
    (cp.lambda_min, tuple(), [[[5, 7], [7, -3]]], Constant([-7.06225775])),
    (lambda x: cp.lambda_sum_smallest(x, 2), tuple(),
     [[[1, 2, 3], [2, 4, 5], [3, 5, 6]]], Constant([-0.34481428])),
    (cp.log, (2, 2), [[[1, math.e], [math.e**2, 1.0 / math.e]]], Constant([[0, 1], [2, -1]])),
    (cp.log1p, (2, 2), [[[0, math.e - 1],
                         [math.e**2 - 1, 1.0 / math.e - 1]]], Constant([[0, 1], [2, -1]])),
    (cp.minimum, (2,), [[-5, 2], [-3, 1], 0, [1, 2]], Constant([-5, 0])),
    (cp.minimum, (2, 2), [[[-5, 2], [-3, -1]],
                          0,
                          [[5, 4], [-1, 2]]], Constant([[-5, 0], [-3, -1]])),
    (cp.min, tuple(), [[[-5, 2], [-3, 1]]], Constant([-5])),
    (cp.min, tuple(), [[-5, -10]], Constant([-10])),
    (lambda x: x**0.25, tuple(), [7.45], Constant([7.45**0.25])),
    (lambda x: x**0.32, (2,), [[7.45, 3.9]], Constant(np.power(np.array([7.45, 3.9]), 0.32))),
    (lambda x: x**0.9, (2, 2), [[[7.45, 2.2],
                                 [4, 7]]], Constant(np.power(np.array([[7.45, 2.2],
                                                                       [4, 7]]).T, 0.9))),
    (cp.sqrt, (2, 2), [[[2, 4], [16, 1]]], Constant([[1.414213562373095, 2], [4, 1]])),
    (lambda x: cp.sum_smallest(x, 3), tuple(), [[-1, 2, 3, 4, 5]], Constant([-1 + 2 + 3])),
    (lambda x: cp.sum_smallest(x, 4), tuple(),
     [[[-3, -4, 5], [6, 7, 8], [9, 10, 11]]], Constant([-3 - 4 + 5 + 6])),
    (lambda x: (x + Constant(0))**0.5, (2, 2),
     [[[2, 4], [16, 1]]], Constant([[1.414213562373095, 2], [4, 1]])),
]


def check_solver(prob, solver_name) -> bool:
    """Can the solver solve the problem?
    """
    atom_str = str(prob.objective.args[0])
    for bad_atom_name in KNOWN_SOLVER_ERRORS[solver_name]:
        if bad_atom_name in atom_str:
            return False
    try:
        if solver_name == ROBUST_CVXOPT:
            solver_name = CVXOPT

        prob._construct_chain(solver=solver_name)

        return True
    except SolverError:
        return False
    except Exception:
        raise


# Tests numeric version of atoms.
def run_atom(atom, problem, obj_val, solver, verbose: bool = False) -> None:
    assert problem.is_dcp()
    print(problem)
    if verbose:
        print(problem.objective)
        print(problem.constraints)
        print("solver", solver)
    if check_solver(problem, solver):
        tolerance = SOLVER_TO_TOL[solver]

        try:
            if solver == ROBUST_CVXOPT:
                result = problem.solve(solver=CVXOPT, verbose=verbose,
                                       kktsolver=ROBUST_KKTSOLVER)
            else:
                result = problem.solve(solver=solver, verbose=verbose)
        except SolverError as e:
            if (atom, solver) in KNOWN_SOLVER_ERRORS:
                return
            raise e

        if verbose:
            print(result)
            print(obj_val)
        assert (-tolerance <= (result - obj_val) / (1 + np.abs(obj_val)) <= tolerance)


def get_indices(size):
    """Get indices for dimension.
    """
    if len(size) == 0:
        return [0]
    elif len(size) == 1:
        return range(size[0])
    else:
        return itertools.product(range(size[0]), range(size[1]))


atoms_minimize = [(a, cp.Minimize) for a in atoms_minimize]
atoms_maximize = [(a, cp.Maximize) for a in atoms_maximize]


@pytest.mark.parametrize("atom_info, objective_type", atoms_minimize + atoms_maximize)
def test_constant_atoms(atom_info, objective_type) -> None:

    atom, size, args, obj_val = atom_info

    for indexer in get_indices(size):
        for solver in SOLVERS_TO_TRY:
            # Atoms with Constant arguments.
            prob_val = obj_val[indexer].value
            const_args = [Constant(arg) for arg in args]
            if len(size) != 0:
                objective = objective_type(atom(*const_args)[indexer])
            else:
                objective = objective_type(atom(*const_args))
            problem = Problem(objective)
            run_atom(atom, problem, prob_val, solver)

            # Atoms with Variable arguments.
            variables = []
            constraints = []
            for idx, expr in enumerate(args):
                variables.append(Variable(intf.shape(expr)))
                constraints.append(variables[-1] == expr)
            if len(size) != 0:
                objective = objective_type(atom(*variables)[indexer])
            else:
                objective = objective_type(atom(*variables))
            problem = Problem(objective, constraints)
            run_atom(atom, problem, prob_val, solver)

            # Atoms with Parameter arguments.
            parameters = []
            for expr in args:
                parameters.append(Parameter(intf.shape(expr)))
                parameters[-1].value = intf.DEFAULT_INTF.const_to_matrix(expr)
            if len(size) != 0:
                objective = objective_type(atom(*parameters)[indexer])
            else:
                objective = objective_type(atom(*parameters))
            run_atom(atom, Problem(objective), prob_val, solver)
